Organizations today are inundated with data, ranging from production logs and financial transactions to customer behaviors and sensor feeds. Hidden within these streams are critical signals that can mean the difference between avoiding costly failures and reacting too late. That’s where outlier detection comes into the picture.
Outlier detection, a key subset of anomaly detection, involves identifying data points that deviate significantly from the norm. These deviations might indicate fraud, defects, safety risks, or emerging trends, making them essential to detect early. But finding them isn’t easy. Traditional analytics tools often miss these events, especially when data volumes are high or patterns are not immediately apparent.
Spotfire, the visual data science platform, is specifically designed to address this challenge. With Spotfire, data professionals can detect outliers using advanced statistical techniques, machine learning models, and interactive visualizations—all without needing to code. The platform supports both data-at-rest and real-time streaming data, enabling faster and smarter responses across industries such as energy, logistics, high-tech manufacturing, and financial services.
Whether you’re preventing unplanned equipment downtime, identifying fraudulent transactions, or improving product quality, Spotfire empowers teams to spot what others miss and turn anomaly detection into actionable insight.
From revenue loss to real-time insight
When Ireland’s insurance market lost over a quarter billion in motor claims, AA Ireland knew the old way of doing things wasn’t enough. Manual reviews, static models, and one-size-fits-all pricing strategies had left companies exposed and reactive.
To compete and protect margins, AA Ireland turned to Spotfire. In just three months, a small analytics team built its first predictive model, connecting customer data across departments, flagging unusual customer behaviors in real time, and identifying fraud patterns as they emerged. With every click, underwriters, marketers, and call center leaders could now see not just what happened, but why. More importantly, they could see what to do next.
The result? A 22 percent increase in revenue through smarter pricing, fraud prevention, and campaign optimization.
What started as an effort to plug financial leaks evolved into a cultural shift toward agility, insight, and data-driven decision-making. It all began by spotting what others missed: the outliers.
Screenshot of a Spotfire analysis featuring outliers and oddities
What are outliers in business data?
In every dataset, there are patterns and exceptions. Outliers are those rare or unexpected data points that deviate sharply from the norm. While they’re sometimes dismissed as noise, these data points often contain critical information. In business, they can signal everything from fraud and malfunction to untapped opportunities or emerging threats.
Outliers and anomalies are frequently used interchangeably, but they aren’t quite the same. An outlier is a statistical deviation, something that falls outside the expected range of values. An anomaly, on the other hand, is a deviation caused by a different underlying process. An outlier may result from natural variability, while an anomaly may reflect a fundamental issue, such as a malfunctioning machine sensor or a fraudulent transaction.
Identifying these deviations is essential for modern enterprises because:
- They distort trends and forecasts.
- They flag operational and financial risks.
- They can highlight hidden value or previously unseen patterns.
Traditional business intelligence (BI) tools often overlook these signals. Spotfire® visual data science changes the game by helping users explore, investigate, and act on data anomalies with clarity and speed.
Using Spotfire for anomaly detection with predictive analytics
Spotfire makes anomaly detection accessible to domain experts and data scientists by combining visual exploration with powerful statistical and machine learning models:
- Visual-first interface: Spotfire enables users to interact with their data through intuitive dashboards, utilizing charts, filters, and AI-powered recommendations to quickly identify potential outliers.
- No-code and pro-code modeling: Users can deploy outlier detection using built-in tools, such as clustering, control charts, and time-series forecasting, or build and embed advanced models in R or Python directly within the visual workflow.
- Support for machine learning techniques: Spotfire supports a range of anomaly detection approaches, including autoencoders, one-class SVMs, and unsupervised clustering, all of which are optimized for high-volume data environments.
- AI-driven recommendations: Spotfire automatically highlights patterns and suggests analyses, enabling users to detect anomalies they may not have considered.
This blend of visual data science and deep analytical power makes Spotfire uniquely effective at uncovering what traditional dashboards miss.
Real-time insights with streaming data
In many industries, anomaly detection is about much more than understanding what went wrong; it’s about knowing in real time, so teams can prevent failures before they occur.
Spotfire integrates with real-time data sources to detect anomalies as they happen. Whether it’s sensor data from a pipeline, traffic flow across a highway network, or financial transactions in a global bank, Spotfire can continuously analyze deviations in real time with streaming data.
Benefits of real-time anomaly detection include:
- Early warning systems for mechanical failures or safety risks
- Live monitoring of transactional data for fraud detection
- Immediate alerts that trigger automated responses or investigations
- Combined live and historical context for better decision-making
This always-on capability ensures organizations stay ahead of the curve, moving from a reactive to a proactive stance.
Learn more about this approach in our Ultimate guide to anomaly detection.
Real-world examples across industries
Spotfire users worldwide are transforming how they respond to data anomalies, and the outcomes speak for themselves.
Eiffage: Strengthening financial control
Eiffage, a major European construction and concessions group, needed to monitor over a million annual supplier invoices for issues like duplicates or irregular payment patterns. Using Spotfire, the finance team gained real-time visibility into ERP workflows and built dashboards that surfaced anomalies as they occurred. This shift enabled auditors to work more independently, reduced financial risk, and improved compliance without placing a burden on IT.
Autostrade per l’Italia: Safer roads through predictive analytics
Autostrade per l’Italia manages 3,000 km of highways, over 4,000 bridges and tunnels, and 2.5 million daily travelers. With Spotfire, they built predictive dashboards using 20 years of operational data, allowing them to detect traffic anomalies, forecast risks, and optimize maintenance. The platform reduced modeling time from 18 hours to under a minute and improved traffic queue prediction accuracy to 97.5 percent.
Spotfire enabled the company to move beyond reactive traffic management, utilizing anomaly detection to maintain a safe and efficient infrastructure.
Better forecasting and decision-making with Spotfire
Outliers are valuable indicators, not merely oddities, revealing potential problems or opportunities when properly analyzed:
- Spotfire facilitates the real-time detection, interpretation, and actioning of outliers through visual tools.
- Integrated statistical modeling and machine learning in Spotfire enable organizations to shift from retrospective metrics to forward-looking insights.
- Scalable across various industries and teams, Spotfire supports diverse applications, including fraud detection, predictive maintenance, and infrastructure monitoring.
Are you ready to see what’s hiding in your data? Discover the powerful capabilities of anomaly detection in Spotfire.